Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A scalable content-addressable network
20013.9k citationsSylvia Ratnasamy, Paul Francis et al.profile →
An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs
Countries citing papers authored by Richard M. Karp
Since
Specialization
Citations
This map shows the geographic impact of Richard M. Karp's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Richard M. Karp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard M. Karp more than expected).
This network shows the impact of papers produced by Richard M. Karp. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard M. Karp. The network helps show where Richard M. Karp may publish in the future.
Co-authorship network of co-authors of Richard M. Karp
This figure shows the co-authorship network connecting the top 25 collaborators of Richard M. Karp.
A scholar is included among the top collaborators of Richard M. Karp based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Richard M. Karp. Richard M. Karp is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Daskalakis, Constantinos, Richard M. Karp, Elchanan Mossel, Samantha J. Riesenfeld, & Elad Verbin. (2009). Sorting and selection in posets. Symposium on Discrete Algorithms. 392–401.10 indexed citations
4.
Lin, Henry, et al.. (2008). Linked decompositions of networks and the power of choice in Polya urns. Symposium on Discrete Algorithms. 993–1002.1 indexed citations
Sharan, Roded, Silpa Suthram, Ryan Kelley, et al.. (2005). Conserved patterns of protein interaction in multiple species. Proceedings of the National Academy of Sciences. 102(6). 1974–1979.507 indexed citations breakdown →
Ratnasamy, Sylvia, Paul Francis, Mark Handley, Richard M. Karp, & Scott Shenker. (2001). A scalable content-addressable network. 161–172.3899 indexed citations breakdown →
10.
Karp, Richard M.. (2001). The genomics revolution and its challenges for algorithmic research. 631–642.2 indexed citations
11.
Xing, Eric P., Michael I. Jordan, & Richard M. Karp. (2001). Feature selection for high-dimensional genomic microarray data. International Conference on Machine Learning. 601–608.416 indexed citations
12.
Etzioni, Oren, et al.. (1997). Fast and intuitive clustering of web documents. Knowledge Discovery and Data Mining. 287–290.178 indexed citations
13.
Etzioni, Oren, Steve Hanks, Tao Jiang, et al.. (1996). Efficient Information Gathering on the Internet (Extended Abstract).1 indexed citations
14.
Karp, Richard M. & Bruce Reed. (1995). When is the Assignment Bound Tight for the Asymmetric Traveling-Salesman Problem.9 indexed citations
Johnson, David S., Michael L. Fredman, Richard M. Karp, et al.. (1983). Proceedings of the fifteenth annual ACM symposium on Theory of computing.39 indexed citations
Held, Michael & Richard M. Karp. (1970). The Traveling-Salesman Problem and Minimum Spanning Trees. Operations Research. 18(6). 1138–1162.900 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.